Artificial Neural Networks for Construction Management…€¦ · · 2018-04-15Artificial Neural...

Journal of Soft Computing in Civil Engineering 1-2 (2017) 70-88

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Artificial Neural Networks for Construction

Management: A Review

P.S.Kulkarni1*

, S.N.Londhe2 and M.C.Deo

3

1. Associate Professor, Vishwakarma Institute of Information Technology, Pune, India.

2. Professor, Vishwakarma Institute of Information Technology, Pune, India.

3. Professor, Indian Institute of Technology, Mumbai, India.

Corresponding author: [email protected]

ARTICLE INFO

ABSTRACT

Article history:

Received: 12 July 2017

Accepted: 23 August 2017 Construction Management (CM) has to deal with a variety

of uncertainties related to Time, Cost, Quality and Safety,

to name a few. Such uncertainties make the entire

construction process highly unpredictable. It therefore falls

under the purview of artificial neural networks (ANNs) in

which the given hazy information can be effectively

interpreted in order to arrive at meaningful conclusions.

This paper reviews application of ANNs in construction

activities related to prediction of costs, risk and safety,

tender bids, as well as labor and equipment productivity.

The review suggests that the ANN’s had been highly

beneficial in correctly interpreting an inadequate input

information. It was seen that most of the investigators used

feed forward back propagation type of the network;

however if a single ANN architecture was found to be

insufficient then hybrid modeling in association with other

machine learning tools such as genetic programming and

support vector machines were much useful. It was

however clear that the authenticity of data and experience

of the modeler are important in obtaining good results

Keywords:

Construction Management,

Artificial Neural Networks,

Training Algorithm,

Sensitivity Analysis.

1. Introduction

Construction industry is highly competitive and faces challenges in the areas of costs of projects,

delays in construction activities, labor productivity, disputes, tenders, bidding prices, safety

aspects, rate of materials, maintenance costs, risk analysis etc. which are highly complicated in

nature. To deal with these challenges, Artificial Intelligence (AI) techniques like fuzzy logic,

case-based reasoning, probabilistic methods for uncertain reasoning, classifiers and learning

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P.S.Kulkarni et al./ Journal of Soft Computing in Civil Engineering1-2 (2017) 70-88 71

methods, Artificial Neural Networks (ANN), Genetic Algorithms and hybrid techniques are

widely used in the field of Construction Management (CM). In the last two decades of twentieth

century, there was a surge in publications dealing with Artificial Intelligent techniques and

especially ANN in various aspects of CM. In 2001, Adeli and Yeh provided a comprehensive

review of such applications made before the turn of the century [1]. The current work presents a

review of about 70 papers published in the area of CM. The objective of the paper is to highlight

the applications of ANN in the following fields of CM: Cost, Productivity, Risk Analysis, Safety,

Duration, Dispute, Unit rate and Hybrid Models. Further critical review of the findings will help

the readers to focus on important areas for potential use and development of ANN in the said

areas of CM. The future scope will facilitate continued research efforts. The paper is further

synthesized as follows: Initially a brief introduction on ANN is presented and is followed by the

assessment of their recent applications in the areas of Cost, Productivity, Risk Analysis, Safety,

Duration, Dispute, Unit rate and Hybrid Models. Discussion and critical review is done in the

preceding section followed by author’s comments on the findings and future scope.

2. Artificial Neural Network

ANN is a soft computing tool, mimicking the ability of human mind to effectively employ modes

of reasoning and/or pattern recognition. ANN as a concept was existing for a long time; however

its application in civil engineering started in late 1980’s primarily in construction activities [1].

ANN’s were found to learn from the relationships between input and output provided through

training data and could generalize the output, making it suitable for non-linear problems where

judgment, experience and surrounding conditions are the key features. ANNs typically comprise

of 3 layers viz. input layer with input neurons, hidden layer(s) with hidden neurons and output

layers with output neurons (figure 1).

Figure 1. Basic ANN architecture

Each neuron in the input layer is connected to each neuron in the hidden layer and each neuron in

hidden layer is connected to each neuron in the output layer. The number of hidden layers and

number of neurons in each hidden layer can be one or more than one. The number of input

neurons, hidden neurons and output neurons constitute the network architecture. Before its

application the network is trained, i.e., the connection weights and bias values are fixed, with the

Output Layer Hidden Layer Input Layer

Neuron Weight Bias Neuron

72 P.S.Kulkarni et al./Journal of Soft Computing in Civil Engineering 1-2 (2017) 70-88

help of a mathematical optimization algorithm and using part of the data set until a very low

value of error is attained. The network is then tested with an unseen data set to judge the

accuracy of the developed model. The network is trained using various training algorithms which

aim at minimizing the error between the observed and network predicted values. The networks

are classified according passage of flow of information either in the forward direction (feed

forward) or in reverse or lateral directions (recurrent network). Generally three-layer feed-

forward or recurrent networks are found to be sufficient in civil engineering practices. Other

types of networks include the counter-propagation ANN, Hamming's network and the radial

basis function network. For details readers are referred to [2, 3, 4, 5, 6].

3. Applications

Since late 1980’s several investigators have applied ANN in civil engineering to carry out a

variety of tasks such as prediction, optimization, system modeling and classification [7].

Applications can be seen in areas of i) construction costs ii) productivity iii) risk analysis and

safety and iv) project duration, disputes and unit rates which are dealt herein.

3.1. Cost

ANNs as a tool is been used to estimate costs of school buildings [8] residential projects,

apartment projects [9,10], cost of structural systems of reinforced concrete skeleton buildings in

early stage [11,12,13], costs of overall building projects [14,15], cost for highway [16,17],

tunnels [18], general overheads [19], cost of deviation in reconstruction projects was predicted

through a single quantifiable measure, the cost performance index [20].Cost estimation of

continuing care retirement community projects were done by developing regression and neural

network models [21]. In 2013, Naik and Kumar utilized ANN for optimizing project cost with

data of of 512 houses in India [22]. Minli and Shanshan in 2012 used ANN to estimate the tender

offer price based on environmental factors, business factors and project factors [23]. ANN was

used for estimating the optimal contingency for an owner’s funding of transportation

construction projects that can achieve solutions that are closer to the optimum than existing tools

[24], modeling of construction project management effectiveness in terms of construction cost

variation [25], predicting maintenance cost of construction equipment [26], pre estimating

models to predict the final cost of highway projects constructed by the New Jersey Department

of Transportation [27], contingency costs for road maintenance activities [28] and project cost

along with schedule success prediction models [29]. ANN is also used as a tool to predict the

cost premium of green buildings based on LEED categories [30]. In 2003, Apanaviciene and

Juodis modeled cost variation and carried out sensitivity analysis to reduce the input variables

from 27 to 12 [25]. For maintenance cost forecasting of selected equipment groups, General

Regression Neural Network (GRNN) models were developed and further compared in terms of

complexity, interpretability, and forecasting accuracy with time series models in 2014 by Yip et

al. [24]. In 2002, Williams developed pre estimating models using ANNs techniques to predict

the final cost of highway projects and bid information was used as input for the models [27].

Development of a prototype model for estimating the cost of building construction projects at


conceptual stage depending on the historical data of projects implemented in Gaza strip between

2009-2012 was done [31].

3.2. Productivity

Applications of ANN exist in the area of productivity of labor and/or equipment. ANN was used

to estimate daily productivity of dozer [32], estimate the productivity within a developing market

for formwork assembly, steel fixing and concrete pouring activities [33], prediction of production

rate values for installation of formwork of beams [34], estimation of the productivity of ceramic

wall construction [35], estimation of labor productivity of marble finishing works [36], labor

productivity for concreting [37,38], for estimating the bricklayer (Builder) productivity [39] and

labor production rate i.e man-hours per unit for pipe installation activity [40,41]. In 2014,

Maghrebi et al. modeled concrete volume productivity in m3/hr with 10 input parameters on

1673 projects. They found that the productivity for a range of concrete volume can be predicted

precisely by ANN, however for productivities less than 5 (m3/hr) and more than 15 (m

3/hr) the

distribution of residuals expanded gradually and for that the underlying process needs attention

[42]. Self Organizing Maps was developed for prediction of construction crew productivity for

ready mixed concrete, formwork and reinforcement crews with work definitions [43].

3.3. Risk analysis and safety

Risk Analysis and Safety are important aspects in CM in for identification of potential risk in the

projects and safety indices are carried out. ANN based procedures had been developed to predict

the likelihood of contractor default in Saudia Arabia [44], and to estimate the risk index for an

expressway construction stage using the principles of system theory, operability, independence

and comparability [45]. ANNs were developed to estimate percentage variation between the

forecasted and actual costs of floats at 30, 50, 70 and 100% completion stages based on 11

significant risk factors [46].). In 2014, Mehidi assessed the risk value for 10 risk factors as

mechanical failure, electrical failure, wrong vendor selection etc. in cement industries in

Bangladesh [47]. In 2007, Elhag and Wang compared techniques of ANN and regression analysis

to estimate the risk score and risk category for bridge maintenance projects [48]. Liu and Guo in

2014 proposed a risk assessment method using rough sets to reduce uncertainties and ANNs [49].

An ANN system was developed so as to identify the cost deviations that occur, due to the

political risk involved in a construction project. The project manager can incorporate the risk

consequences into a bidding decision, and generate revised and updated risk estimates

systematically and easily during the progress of the project. A rating in the form of a percentage

change in cost from the baseline cost forms the output vector for the neural network model [50].

An ANN model was developed to predict safety climate of a construction project and evaluation

of construction employees’ safe work behavior [51, 52]. ANN-based model was developed for

predicting workers’ fatigue in hot and humid environment [53]. In a study ANN and Logistic

Regression were utilized to model the occupational safety and health of construction workers and

performance of the models were assessed by calculating the log-likelihood (LL) ratio [54]. In

2015, Chen and Liu developed model based on Bayesian network for performance assessment of

the subway construction safety in China [55]. Mohammadfam et.al. in 2015, used chain

analytical approach which included rough set theory and ANN modeling, and modeled the


factors affecting health of workforce and predicting severity of occupational injuries [56]. In

2013, Goh and Chua used neural network to study relationship between elements of safety

management and accident severity and discussed on proactive management of accidents [57].

3.4. Duration, dispute and unit rate

In 2001, Leung et al. modeled the hoisting times of tower cranes using two types of

architectures, namely, Multi-Layer Feed Forward (MLFF) with back-propagation based (BP)

learning and General Regression Neural Network (GRNN) with genetic algorithm based learning

[58]. In 2003, Cheng and Ko developed an object-oriented Evolutionary Fuzzy Neural Inference

System (OO-EFNIS) for predicting a subcontractor’s performance and duration estimation of

slurry wall [59]. Sawalhi and Hajar in 2016 used ANN to study more accurate selection of the

best contractor in Gaza strip. The network selected for the study were either MLP (multi layer

perceptron) or GFF (General feed forward) [60]. ANN with back propagation was developed for

estimation of cost and duration of 2 highway projects [61]. In 2000,Cheung et.al. reported using

ANN to classify projects in accordance with project “dispute resolution satisfaction (DRS)”

which also identified the sensitive variables that distinguish projects with adverse DRS and

favorable DRS [62]. In 2007, Chau used Particle Swarm Optimization based network for

prediction of outcomes of construction litigation [63]. In a study identification of qualitative

parameter were done and an Artificial Neural Network model was developed to minimize the

construction dispute resolution and reduce the cost of the project by optimizing the parameters

[64]. Yahia et.al. in 2011 developed project time prediction model using number of change

orders [65]. Scientific prediction of the economic strata (The difference between actual direct

costs and prevailing market rates) was done in 2015 by Mwiya et al. using ANN by determining

the proportionate breakdown of the cost factors in a given construction unit rate [66]. Recently

Mensah et al. in 2016 predicted actual durations of bridge construction projects in which

principal component analysis was employed to determine the significant items which can be used

for model development and in detecting the multi-co linearity within the data base [67].

3.5. Hybrid Models

Owing to the complex nature of modeling CM parameters, hybrid models were created to

combine the advantages of two different tools for possible better performance of the model. It

was shown that ANN was capable of learning from the data provided but it cannot explain the

reasoning behind the input–output mapping process while fuzzy reasoning provides a systematic

reasoning method which leads to development of neuro-fuzzy systems [68, 69]. A neuro-fuzzy

model based on the locally linear model tree algorithm was employed by Vahdani et al. in 2011

to precisely estimate the overall performance (qualitative and quantitative factors) of projects

[70]. A real case study was selected in construction industry in Iran to appraise and select the

candidates for the investment (cladding in this case). Genetic Algorithm (GA) was utilized to

optimize the parameters (processing elements or neurons) of the back-propagation network for

predicting the construction costs of residential buildings [14]. ANN technique was used for

development of parametric cost estimating model which can be used in the early stage of the

project life cycle for sterile buildings as pharmaceutical and food projects in Egypt and further

Genetic algorithm was used for optimizing the weights [71]. Neuro fuzzy technique is also been


used for creation of a classification system for Russian Federation according to the

environmental safety level and forecasting of changes in their development due to the changes in

the level of environmental safety [72]. Integrating a neuro fuzzy system with conceptual cost

estimation to discover cost-related knowledge from residential construction projects was

proposed. The data used in this proposal was based on historical data from previous construction

projects collected by the Ministry of Construction of PRC in the years between 1996 and 2002

[73]. In 2009, Cheng et al. proposed web-based conceptual cost estimates for construction

projects, using an Evolutionary Fuzzy Neural Inference Model. Data were collected from 28

construction projects spanning the years from 1997 to 2001 in Taiwan [74]. In a study historical

cost data of continuing care retirement community projects were compiled to develop regression

and neural network models. The study showed that by using regression and neural network

simultaneously a satisfactory conceptual cost model can be achieved [21].

4. Discussion and Critical Review

The foregoing sections presented an outline of applications of ANN in CM grouped in major

areas. A detail discussion and critical review of these applications is presented in this section

with respect to Method of data acquisition, Input parameters, data/sample size, network

architecture, type of network, training, transfer function, over fitting, learning and momentum

rate, performance function, number of epochs, performance measure, comparison with other

methods and general comments.

4.1. Method of data acquisition

In the works discussed, the data were obtained from past reports or by questionnaire survey,

interviews, and case studies from construction projects. Unavailability of certain data can be a

limitation for the model developed and thus a comprehensive dataset is required for achieving a

meaningful output [30, 20, 52, 29]. In a study data is collected through structured questionnaire

and expert interviews which were used to identify the most influential factors on contract

awarding system in Gaza Strip [60]. Fifty four questionnaires, as a response rate of 77% of the

total number of questionnaires, have been correctly answered and submitted. For the need of

many data to develop the neural network model, many historical projects done between 2010 and

2012 in Gaza Strip were collected from municipalities, government ministries, engineering

institutions, contractors and consultants. [60]. In a study for developing Building Construction

Projects Cost Estimating, Eighty questionnaires were distributed to various engineering

institutions. Fifty-seven questionnaires with a response rate 71% have been correctly received

[31]. The questionnaires typically consisted of identified factors that affect parametric cost

estimate of projects. Thirteen cost parameter in skeleton phase and eighteen finishing phase

parameters which were identified from literature were evaluated [31]. To determine productivity

of labor a structured closed-ended questionnaire has been developed for gathering data on the

basis of subjective judgment of productivity factors and the related productivity [38]. In a study

direct observation method was used for collecting the data in this research. Pilot study was done

by selecting ten construction projects in different parts of Iraq. Work sampling approach was

used to measure the production rates at site to calculate duration of activity on daily basis at


specific time interval using stop watch [36]. For establishing reasonable safety evaluation index

system is the key to the comprehensive evaluation of construction safety. A scientific

comprehensive evaluation index system of building construction safety has been established

based on analytic hierarchy process, which combined with the expert opinion and the suggestion

[75]. The data set used in a research to predict the best contract in Gaza strip, the data set used

was part of a larger dataset collected from construction workers during 2005 and 2006 in the

Cincinnati/Tri-State area using primarily the Work Compatibility survey. The Work

Compatibility Model is a multidimensional diagnostic tool of human performance that measures

the level of synchronization between the workforce and the work environment and contains 11

sections with 166 questions [60].

4.2. Input Parameters

Selection of input parameters to develop a network relies on a priori knowledge of the subject,

the characteristic of data source, past experience and availability of data. The accuracy of ANN

outcome depends on the quality of training data and the ability of the developer to choose truly

representative input information. It was noticed that correct selection of input parameters through

a sensitivity analysis was very helpful in achieving better results [11, 12, 25]. Identification of

input can also be done by methods such as Delphi method, systems engineering, and factor

analysis [49, 51]. Statistical analysis of input parameters was made to understand the influence of

various parameters on the labor productivity [34]. In 2003, Apanaviciene and Juodis modeled

cost variation using 12 influential factors including seven for project manager category, one for

project team, two for planning and two for organization and control category [25]. Tools as factor

analysis, principal component analysis and Box and Whiskas method for eliminating outliers

were utilized [51, 43, 66]. While input selection can be done with the help of statistical

parameters appropriate domain specific knowledge significantly helps in this regard [76]. The

input parameters selected for few of the above mentioned works are: In 2012, Minli and

Shanshan used factors as environmental factors, business factors and project factors [23]. Input

parameters as construction year, building type, city, and actual construction cost, scores achieved

from LEED categories were used to predict cost of premium Green buildings [30]. Kim et al. in

2013 used 10 input parameters as year, budget land and school building specifications to predict

cost of school building [8]. ANN model in predicting the productivity of concrete in m3/hr for

Sydney area utilized input parameters as weekday, starting time of first delivery, amount of

ordered concrete, longitude and latitude of project, number of orders received, number of

assignments delivered [42]. To predict the likelihood of contract default, 23 factors related to

contractor characteristics, 2 -particulars of the contract and 3 nature of the project were used as

input variables [44]. In 2012, Chenyun et al. determined a risk index for an expressway

construction stage using the principles of Scientific, system, theory for practice, operability,

independence and comparability [45].Prediction and evaluation of construction employees’ safe

work behavior was modeled using ANN with safety indices as inputs [52]. Input parameters as

WBGT (Wet-bulb globe temperature), age, alcohol-drinking habit, smoking habit, work duration,

job nature with output as rating of Perceived exertion were utilized for determining the fatigue of

construction workers [53]. Various cost estimations involved in construction works depend on

either estimator-specific factors or design and project-specific factors [77]. 21 input parameters


as bid prize, capital of the company, liquidity, debt volume, banking facilities etc. were selected

to predict the best contractor in Gaza strip. Sensitivity analysis was carried out to evaluate the

influence of each input parameter to output variable for understanding the significance effect of

input parameters on model output [60]. In the research four sections (due to obvious relation

with occupational disorders and diseases among construction workers, but yet unknown in

severity), were included i.e physical environment, economical factors, muscular activities, and

experience at work which were subjectively answered by participating workers on a Likert scale

of 5 (from ‘not at all’ to ‘entirely’) as input parameters [54]. Input parameters as political risk

factors: Firm's relationship to the government, Firm's relationship to the power groups ,

Involvement of local business interests, Impact of regional and external factors, Nationalistic

attitudes toward the firm and Project desirability to the host country were selected to predict the

percentage change in cost from the baseline cost forms [50].

4.3. Normalization of data

Neural network training could be made more efficient by using neural network processing

functions which transforms inputs into better form for the network use. Such scaling should

improve the density of the data over the problem domain and allow the neural networks to

converge faster and later to generalize better outputs [33].It is especially useful for modeling

application where the inputs are generally on widely different scales. Different techniques can

use different rules such as max rule, min rule, sum rule, product rule etc. [78]. In most of the

papers reviewed, preprocessing of the data in the form of normalization between -1 to 1 was

done to increase the performance of thee network [11, 30]. Normalization of the data using Z-

scores was used which leads to an increase in performance of the trained ANN [61].

Normalization of data between 0 and 1 was also seen prominently [64].

4.4. Data / sample size

In case of the adequate sample size to be used while training and testing of the network as well

as the split of data into training and testing, a large variation was observed in the past works. In

general 60%-80% of the data was used for training and remaining for validation (as one of the

step to take care of over fitting) and or testing in the papers reviewed. Data of direct cost of

school buildings were presented to ANN with 20 for testing, 67 cross validation and 130 for

training [8]. A network was developed which consists of 27 project data which was trained with

85% of the data set (33 records) that were randomly picked, while 15% (6 records) were used for

testing [41]. Out of the total sample consisting of 506 input-output pairs in their work Elhag and

Wang in 2007 used around 77 % for training and remaining for testing while Minli and Shanshan

(2012) employed 30 pairs for training and one only for testing [23,48]. It remains to be seen how

far such validation is sustained. An appropriate splitting of data is required in such a way that the

model should be trained with all data patterns to predict meaningful results. In 2016, Sawalhi and

Hajar utilized 91 tenders for selection of best contractor in Gaza strip. Of the 91 tender’s data,

66% were utilized for training, 18% for Cross validation set and 16% for testing phase [61].

Selection of subject for data collection also plays a vital role. Out of 191 subjects to assess the

performance of ANN and Logistic Regression by calculating the log-likelihood (LL) ratio, 188

were male and 1 was female, and the gender of two subjects was missing. The average height


and weight of the sample were 179.8 cm and 87.42 kg, with standard deviation of 6.49 cm and

15.97 kg, respectively. The height of two subjects and weight of five subjects were missing.

Seventy-two subjects (38.29%) were smokers and 116 subjects (61.70%) and health 137 were

non-smokers, and the smoking status of three subjects was missing. Because of the large size of

the initial dataset and limited resources available for high-volume computation and data analysis,

the dataset was divided into 160 subsets based on outcome variables and for each subset,

separate LR and ANN models were developed, 70% of each data set was used to train the

network and the remaining 30% was used to validate the results [54]. The sample size for

Development of Awarding System for Construction Contractors in Gaza Strip was 54

respondents which consist of 33% as public owners, 6% as donors, 19% as NGOs, 15% as

implementing agencies, 11% as consultants and 17% as other organizations [60]. 169 was the

data set that were divided logical randomly, according to literatures, into three sets 66% for

training, cross-validation 16% and 15% of test data for estimation construction cost of building

projects [31]. Examining the sufficiency of the data for modeling purposes, scatter plots were

produced to draw a trend line between each independent variable and the calculated CPI (cost

performance index) in various case studies, which showed that all trend lines exhibit logical

relationships, thus confirming the sufficiency of the data [20].

4.5. Presentation of data

Artificial networks only deal with numeric input data. Therefore, the raw data must often be

converted from the external environment to numeric form. In the study to predict the best

contractor in Gaza Strip, the data were converted to numeric form by dividing the inputs for each

factor to ranges which were represented as numeric [60]. Data was also presented in a

combination of binary and raw form for Analysis of construction Dispute Resolution with input

parameters as project month, year location in binary form and parameters as labour estimate,

equipment and material estimate in raw form [64]. In In a research to develop Neural Network

Model for Building Construction Projects Cost Estimating, the data was textual and numeric, and

it was encoded to numeric form [31]. Data in the form of numerical scale from 1-5 as positive

factors and -1 to -5 for negative factors was given to network to predict the productivity of labor

[38]. In 2016, Aswed in their study for productivity estimation model for bricklayer, two classes

of independent variables are found: objective and subjective variables [39]. The measurable

(objective) variables according to their unit of measure, such as age and experience are measured

in years, gang number is measured in number, wall thickness is measured in centimeters, and

wall length is measured in meters. Coding system is used to measure the qualitative (subjective)

variables, for example, the gang health can be classifies to bad moderate and good and assigns

them the value 1, 2 and 3 respectively. A quantification of each input variable (into one of seven

values) was done in a study to model the political risks in construction industry. For example,

Attribute 1 (Firm's Relationship to Government) had a range value between 1 (Very Good) to 7

(Very Poor). When an attribute is labeled as “very good,” it refers to the best favorable condition

that an expert can think of regarding that attribute, while an attribute labeled as “very poor”

refers to the maximum worst condition that an expert can think of regarding that attribute [50]. In

a study to predict productivity of labors the inputs were divided into objective and subjective

parameters. The objective variables like age(in years), experience(in years), Number of


labor(number) etc. and subjective variables as the security conditions (category assign value

between 1 and 2), health (category- which specifies as good, moderate and bad, it assigns them

the values of 1, 2 and 3, respectively. While the weather condition; sunny (1), rainy (2). The site

conditions can be classifies to complex and simple and assigns them the value 1 and 2,

respectively. Whereas the scale of 1 and 2 represent near and far, respectively about availability

of construction materials [36].

4.6. Network Architecture

The first step in designing a network is to determine the number of input nodes, hidden nodes

and output nodes. The selection of these parameters is problem dependent and there is no simple

and clear cut method for these. In most of the papers studied, Neural network development was

done with 3 layers i.e input layer, single hidden layer and output layer with few exceptions e.g

for cost estimation of highway project in Thailand dataset the architecture used was 4-10-6-1

[16]. The no. of neurons in hidden layers was computed using trial and error method [34, 46].

The optimum numbers of hidden neurons were also computed by calculating correlation (r)

between the actual and predicted outputs against an increasing number of hidden neurons as they

are added to the network and graphed [44]. MAPE and MSE of the training were computed and

plotted on a graph corresponding to the number of hidden neurons and the no. of neurons with

lowest MAPE and MSE were selected [51]. An attempt was done to increase the number of

hidden layers from 2 to 10 and a slight improvement in results was observed; however, the

differences were not significant [42]. The Multilayer Perceptron (MLP) developed to select best

contractor in Gaza strip includes one input layer with 21 input neurons and one hidden layer with

(30 hidden neurons – selection through trial and error method) and finally one output layer with

one output neuron (the best contractor) [60]. Different network architectures were seen for

different factors as: analyzing the economical condition factors had the 7–5–6 network structure.,

for work experience factors was 15–6–6, for physical, environment 10–5–6, and the network

structure for muscular activities for the upper body joints was 15–6–6 and for lower body joints

22–6–6, in which the numbers represent the number of neurons (nodes) in input, hidden, and

output layers, respectively [54].

4.7. Type of Network

In majority of works discussed in the preceding sections the FFBP type of architectures was used

[11, 19, 38, 39, 51, 67]. For estimating labor production rates in 2000, Lu et al. used Probability

inference neural network (PINN) which contains Kohonen classifier and a Bayesian layer. The

developed PINN model outperforms the back-propagation neural network model in terms of

point prediction accuracy, by coming closer to the actual output values [40]. Modeling

construction Unit Rate Factor was done using Kohonen Self Organizing Map network was used

in 2015 by Mwiya et al. [66]. ANN ensemble models (bootstrap aggregating and adaptive

boosting ANNs classifiers) were also found to produce better results than FFBP when used for

project cost along with schedule success prediction models [29]. A construction cost forecasting

model based on RBF neural network was developed which showed a better performance over

ANN (back propagation BP) [79]. In 2015, Bayram et.al. carried out a study to estimate

construction costs in Turkey using multi layer perceptron (MLP), Radial Basis Function (RBF)


and unit area cost method and it was found that the performance of RBF was superior to MLP

[15]. A SOM (Self organizing Map) based model was developed to both analyze the effect of

various variables on crew productivity and to predict the crew productivity values [43]. Leung et

al. (2001) utilized General Regression Neural Network (GRNN) with genetic algorithm based

learning for modelling hoisting times of tower cranes [58]. Object-oriented Evolutionary Fuzzy

Neural Inference System (OO-EFNIS) was used for predicting a subcontractor’s performance

and duration estimation of slurry wall [59]. Particle Swarm Optimization (PSO) based network

for prediction of outcomes of construction litigation was developed to give a successful

prediction rate of up to 80%. The PSO-based perceptron was found to work better and faster

than BP-based perceptron [63]. Thus, though in most of reviewed papers use of FFBP network

was widely seen, other types of networks were seldom used. Thus a need arises to explore the

use of other network types for various applications and standardized the need of a particular

network to a particular application. In 2016, Sawalhi and Hajar utilized MLP (multi layer

perceptron) or GFF (General feed forward) for predicting the best contractor [60]. In a study,

safety evaluation model was established for building construction with taking expert scoring as

network input, security class as the output based on Hopfield neural network. Research shows

that Hopfield neural network has very strong memory and association function, and reflects the

digital characteristics of sample data [75].

4.8. Training

As regards training methods Jacobian matrix [56], gradient descent [34], Levenberg-Marquardt

[10, 13, 51], and resilient back propagation [25,28], methods had yielded remarkable results.

Bayesian regularization back propagation was used [61].The scaled conjugate gradient back

propagation called trainscg is used for the early stopping method and Regularization is done in

an automated fashion by using the Bayesian framework which is implemented in the training

function trainbr [38].

4.9. Transfer Function

In general hyperbolic tangent and, log-sigmodial type functions were common. Use of non-

sigmodial type (polynomial, rational function and fourier series) transfer functions were seldom

seen and need to be explored in areas of CM. In a study various transfer functions and the results

in terms of root mean square were compared displaying the sigmoidal as the best transfer

function [71]. The sigmoid function is a good candidate to be used as activating functions

because they are continuous and derivative at all points and very similar to LR model, which can

help analyze similar non-linearity among variables [54]. In a study to predict the productivity of

labor, the following points were encountered during training of the networks: (1) using logsig

function in the networks trained with trainscg brought less accurate results; however, the trainbr

exhibited a good response when using logsig; (2) utilizing the tansig function in the output layer

of the networks trained with trainbr caused failing of the networks [38].

4.10. Over fitting

Sometimes, ANNs have over fitting problems and to address this problem, simple architecture of

the network, sufficient numbers of data samples, provision of the dataset for validation, use of


faster convergence, and early stopping criteria are recommended during development of network.

In order to prevent networks from over fitting and improve their generalization, early stopping

and Bayesian regularization were implemented [38]. In order to avoid the problem of fitting this

problem, the researcher utilized a trial-and-error approach by running each ANN model with

different values for some parameters such as learning rate and acceptable error. This method

helped to identify the best performing ANN model [54].

4.11. Learning and Momentum rate

Mention of learning rate and momentum rate can be seen in few papers which is either

considered or adopted by software [11, 19, 20, 34, 36, 46]. It was found that a specific rule could

not be found for changing the learning rate as well as momentum rate. Few of the papers

mentioned that the learning rate adopted were 0.0001 [23] and 0.5 [19, 34] and maximum

momentum rate of 0.9 [35] for developing a network. A learning rate of 0.15 was arbitrarily

chosen to ANN model for political risk in construction Industry, since larger learning rates often

have been found to lead to oscillations in weight changes resulting in a never-ending learning

process. One way to allow faster learning without oscillations is to make t was he weight change,

in part, a function of the previous weight changes. A momentum coefficient represents this

portion of the weight change. In this study, a coefficient of 0.7 was found to perform well [50].

4.12. Performance Function

Generally network were trained to achieve low Mean squared error [19, 23, 38,44,63], absolute

error [45,46,49] between the network predicted and observed values. In 2003, Apanaviciene and

Juodis utilized modified regularization error function to study the network performance [25]. For

Bayesian Regularization- the sum of squares of the network errors were used to predict the

construction labor productivity [38].

4.13. Number of Epochs

A performance percentage graph with no. of iterations/epoch was generally seen in papers

reviewed [11, 19, 38, 62]. To mention a few studies, In 2014, Mehidi showed that the highest

performance percentage obtained after 9000 iterations and the performance percentage was

95.45% with lowest possible error of 0.00045 in an application to assess risk in cement industries

[47]. For predict the duration of concrete operations, in 2014, Maghrebi et al. utilized Mean

square error as performance function and the best result was obtained at 14 epoch with

MSE=10.94 [42]. In a study epoch number 10,000 was found adequate for the final training

process in a series of test runs. The performance of the network deteriorated for fewer iterations

than 10,000 and the network began to memorize the output values for iterations more than

10,000 [11]. 20, 000 iterations were planned for the final training process, as this was found

adequate in a series of test runs during modeling of political risk in construction Industry [50].

4.14. Performance measure

In most of the works listed above, the model performance was validated by using statistical

measures of correlation coefficient, coefficient of determination, mean squared error, mean


absolute percentage error and percentage error [11, 36, 70]. Average accuracy percentage (AA%)

was utilized with other performance parameters [11]. Most of the researchers adopted only one

or two measures to judge the model performance and very few utilized more than two

performance indicators [20, 34, 37, 48, 51]. Adopting a set of linear and non-linear error

measures as performance indicators for a developed ANN model was beneficial as observed in

2014 by Deshpande et al. [76]. In 2014, Lhee et al. judged the model performance through one-

step and two-step kurtosis in an application related to transportation construction projects. Visual

interpretation of results in terms of Network interpretation diagram can be utilized [24]. Lu et.al.

in 2000 estimated labor production rates using the probability inference neural network (PINN)

model and presented them in the form of probability density function graphs[10]. In 2001,

AbouRizk et.al. presented histograms reflecting the likelihood of labor production rate with

Kohonen Learning Vector Quantization type of classification networks [41]. To select the best

contract in Gaza strip, the performance measure of accuracy performance (AP) was utilized

along with correlation coefficient and mean absolute error. The accuracy performances defined

as (100−MAPE) %. [60]. The LL value is an estimate that is parallel to F and R2

and was used to

evaluate the goodness of fit for ANN and Logistic Regression (LR) models. LL is the criterion

for selecting parameters in an LR model. When two models were compared, the larger the LL

value, the better was the model performance [54].

5. Comparison with Other Methods

In some of the works discussed above the results of ANN models were compared with models

made using other tools, e. g., statistical regression analysis, case based reasoning, genetic

algorithm and support vector machine. It was seen that generally the ANN models performed

better than these [8, 9], although data adaptability was noted to be better in some alternatives [9].

To predict the owner’s contingency on transportation construction projects, one step ANN model

predicts the contingency amount directly as its output and two step predicts the contingency rate

which is then multiplied by the adjusted original contract amount to get the contingency amount.

Both the one-step and two-step ANN-based models demonstrated good learning for the training

dataset based on the high correlation values of 0.827 and 0.863, respectively [24]. In a study

Support Vector Machines were found to work better than ANN based model for prediction of

cost of buildings in Taiwan [29]. However in a study to estimate costs of school building in

Korea it was seen that ANN model gave more accurate estimation results than the Regression

Analysis and Support vector regression models [9]. In a study it was seen that Neural networks

and multiple regression technique can be used to model hoisting times of tower cranes however

the predictive performance of GRNN with genetic algorithm models was found to be better than

that of the multiple regression model and the MLFF network with BP algorithm in modeling the

hoisting times [58]. In study carried out 160 LR and ANN models were made and their

performances were assessed by estimating the LL ratio. The result of a t-test showed that the

ANN models performed significantly better than LR models (P-value,0.001). This means that

regardless of the type of variables, ANN models can predict the outcome more accurately than

LR models. A need for Hybrid models arises which can display higher accuracy and can utilize

the advantage of other tools. The lack of homogeneousness in the data set used in research,


choice of training algorithm, and ANN model structure are considered to be the potential reasons

that 33 of LR models outperformed the ANN models [54]. Comparison of expert and the

predicted values by the neural network for the complete data set of the political source decision

variables was done which shows the neural network solutions to be accurate [50].

6. General Comments

Some of the constraints while using ANNs in CM can be summarized as absence of structured

methodology to decide on various control features and parameters and its black box nature which

couldn’t explain the underlying input-output process [8]. Use of input parameters with ordinal

variables only can be seldom seen in literature and it needs to study further for better

understanding. So far, all the studies have compared the ANN and LR models only with binary

or continuous variables [54]. The knowledge of relationship between input and output

parameters modeled using ANN is locked up in trained weights and biases. A need thus arises to

unlock it and thereby interpret the results. Though in most of the studies reviewed it was shown

that ANN model performs better than the other models for accuracy of estimation, it will be

difficult for the user to understand and explain the estimation results from this ANN model [9].

Dedicated work towards this area will popularize the technique with regular users. Acceptability

of ANNs for routine practical works can increase if its portability (in terms of an easy GUI or

interface with commonly used software) and standardization issues e.g standardization of

minimum accuracy level required for acceptance of a model, minimum and important input

parameters required for a particular project with specified output, minimum learning rate and

momentum rate, minimum number of neurons in hidden layers etc. are addressed. The developed

neural network-based political risk model can be integrated into spreadsheets used for cost

estimation. Such integration facilitates the practical use of the proposed approach by consulting

engineers performing supervision and by construction managers responsible for project risk

management and control functions [50].

Case studies were considered for collection of data and further development of model. However

only few present the use of them for practical applications [22, 25, 57, 72]. Limited studies were

observed in using ANN in the area of safety evaluation in hot and humid environments [53] and

in construction unit rates [66].

7. Concluding Remarks

In the current paper an extensive review of past works dealing with recent applications of ANN

in areas of Cost, Productivity, Risk Analysis, Safety, Duration, Dispute, Unit rate and Hybrid

Models is done. The review confirms the usefulness of ANNs in carrying out a variety of

prediction, classification; optimization and modeling related tasks in areas of CM. ANNs are

based on the input-output data in which the model can be trained and can always be updated to obtain

better results by presenting new training examples. ANN thus has significant benefits that make it a

powerful tool for solving many problems in the field of CM. However large scope is still found to

exist in experimenting with variety of network architectures, training algorithms and hybrid type

of methods, which could lead to a higher level of model performance. Acceptability of ANN for


routine use in CM can be increased if clear guidelines to select input, network architecture,

learning algorithms and other network control parameters are evolved from an exhaustive

assessment of all past works. Providing a standard benchmark for determining the accuracy level

of the construction proposals will help in increase use of ANN in CM. Large scale attempts in

future to unlock potential knowledge in the network system can also go a long way in increasing

user confidence in the ANN use. Only few instances are seen pertaining to use of developed

ANN models for practical applications. Implementation of ANN for live projects and a step

towards understanding the user related problems towards implementation of the same should be

done.

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